Cluster-based Optimization of Urban Transit Hub Locations: Methodology and Case Study in China

Choosing proper locations of urban transit hubs has always been one of the critical concerns facing urban transportation planning agencies in China. This study proposes a mixed integer optimal location model for urban transit hubs, with the objective to minimize the demand-weighted total travel time, when explicitly taking into account Traffic Analysis Zones (TAZs) as demand origins or destinations in a target urban area. An Integer Non-linear Programming (INLP) reformulation was developed to significantly reduce the number of variables. Bilinear constraints in the proposed INLP formulation were then re-modeled into linear functions to ensure global optimal solutions obtained. The model was successfully applied to optimize the hub locations in Suzhou Industrial Park, China, with the result of significantly improved system performance. The impacts of several critical factors, such as the number of hubs and the travel time discount coefficient on the system performance are also investigated.